202 lines
8.4 KiB
Python
202 lines
8.4 KiB
Python
import os
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import sys
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__package__ = "scripts"
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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import argparse
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import torch
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import warnings
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import gradio as gr
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from queue import Queue
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from threading import Thread
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from PIL import Image
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from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
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from model.model_vlm import MiniMindVLM, VLMConfig
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from transformers import logging as hf_logging
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hf_logging.set_verbosity_error()
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warnings.filterwarnings('ignore')
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def init_model(lm_config):
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tokenizer = AutoTokenizer.from_pretrained('../model')
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if args.load == 0:
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moe_path = '_moe' if lm_config.use_moe else ''
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ckp = f'../out/sft_vlm_{lm_config.hidden_size}{moe_path}.pth'
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model = MiniMindVLM(lm_config, vision_model_path="../model/vision_model/clip-vit-base-patch16")
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state_dict = torch.load(ckp, map_location=args.device)
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model.load_state_dict({k: v for k, v in state_dict.items() if 'mask' not in k}, strict=False)
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else:
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transformers_model_path = '../MiniMind2-V'
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tokenizer = AutoTokenizer.from_pretrained(transformers_model_path)
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model = AutoModelForCausalLM.from_pretrained(transformers_model_path, trust_remote_code=True)
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model.vision_encoder, model.processor = MiniMindVLM.get_vision_model("../model/vision_model/clip-vit-base-patch16")
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print(f'VLM参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万')
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vision_model, preprocess = model.vision_encoder, model.processor
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return model.eval().to(args.device), tokenizer, vision_model.to(args.device), preprocess
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class CustomStreamer(TextStreamer):
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def __init__(self, tokenizer, queue):
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super().__init__(tokenizer, skip_prompt=True, skip_special_tokens=True)
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self.queue = queue
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self.tokenizer = tokenizer
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def on_finalized_text(self, text: str, stream_end: bool = False):
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self.queue.put(text)
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if stream_end:
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self.queue.put(None)
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def chat(prompt, current_image_path):
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global temperature, top_p
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image = Image.open(current_image_path).convert('RGB')
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pixel_values = MiniMindVLM.image2tensor(image, preprocess).to(model.device).unsqueeze(0)
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prompt = f'{lm_config.image_special_token}\n{prompt}'
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messages = [{"role": "user", "content": prompt}]
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new_prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)[-args.max_seq_len + 1:]
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with torch.no_grad():
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inputs = tokenizer(
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new_prompt,
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return_tensors="pt",
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truncation=True
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).to(args.device)
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queue = Queue()
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streamer = CustomStreamer(tokenizer, queue)
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def _generate():
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model.generate(
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inputs.input_ids,
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max_new_tokens=args.max_seq_len,
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do_sample=True,
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temperature=temperature,
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top_p=top_p,
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attention_mask=inputs.attention_mask,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id,
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streamer=streamer,
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pixel_values=pixel_values
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)
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Thread(target=_generate).start()
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while True:
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text = queue.get()
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if text is None:
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break
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yield text
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def launch_gradio_server(server_name="0.0.0.0", server_port=7788):
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global temperature, top_p
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temperature = args.temperature
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top_p = args.top_p
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with gr.Blocks() as demo:
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gr.HTML(f"""
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<div style="text-align: center; margin-bottom: 1rem; display: flex; align-items: center; justify-content: center;">
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<img src="https://www.modelscope.cn/api/v1/studio/gongjy/MiniMind/repo?Revision=master&FilePath=images%2Flogo2.png&View=true"
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style="height: 60px;">
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<span style="margin: 0 0 0 1rem;font-size:40px;font-style: italic;font-weight:bold;">Hi, I'm MiniMind2-V</span>
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</div>
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""")
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with gr.Row():
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with gr.Column(scale=3):
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def get_current_image_path(image):
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global current_image_path
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if image is None:
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current_image_path = ''
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return
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current_image_path = image
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return current_image_path
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with gr.Blocks() as iface:
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with gr.Row():
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image_input = gr.Image(type="filepath", label="选择图片", height=650)
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image_input.change(fn=get_current_image_path, inputs=image_input)
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def update_parameters(temperature_, top_p_):
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global temperature, top_p
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temperature = float(temperature_)
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top_p = float(top_p_)
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return temperature, top_p
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with gr.Blocks() as iface_param:
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with gr.Row():
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temperature_slider = gr.Slider(label="Temperature", minimum=0.5, maximum=1.1, value=0.65)
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top_p_slider = gr.Slider(label="Top-P", minimum=0.7, maximum=0.95, value=0.85)
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temperature_slider.change(fn=update_parameters, inputs=[temperature_slider, top_p_slider])
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top_p_slider.change(fn=update_parameters, inputs=[temperature_slider, top_p_slider])
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with gr.Column(scale=6):
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def chat_with_vlm(message, history):
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if not message:
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yield history + [("错误", "错误：提问不能为空。")]
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return
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if not current_image_path:
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yield history + [("错误", "错误：图片不能为空。")]
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return
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image_html = f'<img src="gradio_api/file={current_image_path}" alt="Image" style="width:100px;height:auto;">'
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res_generator = chat(message, current_image_path)
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response = ''
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for res in res_generator:
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response += res
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yield history + [(f"{image_html} {message}", response)]
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chatbot = gr.Chatbot(label="MiniMind-Vision", height=680)
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with gr.Row():
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with gr.Column(scale=8):
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message_input = gr.Textbox(
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placeholder="请输入你的问题...",
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show_label=False,
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container=False
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)
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with gr.Column(scale=2, min_width=50):
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submit_button = gr.Button("发送")
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submit_button.click(
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fn=chat_with_vlm,
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inputs=[message_input, chatbot],
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outputs=chatbot
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)
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# # 添加示例问题
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# gr.Examples(
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# examples=["描述一下这个图片的内容。", "画面里面有什么？", "画面里的天气怎么样？"],
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# inputs=message_input)
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demo.launch(server_name=server_name, server_port=server_port)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description="Chat with MiniMind")
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parser.add_argument('--lora_name', default='None', type=str)
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parser.add_argument('--out_dir', default='out', type=str)
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parser.add_argument('--temperature', default=0.65, type=float)
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parser.add_argument('--top_p', default=0.85, type=float)
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parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str)
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parser.add_argument('--hidden_size', default=512, type=int)
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parser.add_argument('--num_hidden_layers', default=8, type=int)
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parser.add_argument('--max_seq_len', default=8192, type=int)
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parser.add_argument('--use_moe', default=False, type=bool)
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parser.add_argument('--stream', default=True, type=bool)
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parser.add_argument('--load', default=0, type=int, help="0: 原生torch权重，1: transformers加载")
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args = parser.parse_args()
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lm_config = VLMConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers,
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max_seq_len=args.max_seq_len, use_moe=args.use_moe)
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model, tokenizer, vision_model, preprocess = init_model(lm_config)
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launch_gradio_server(server_name="0.0.0.0", server_port=8888)
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